{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:R3BZTE2M7LNBSKJT2RSCI4QFXQ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f2f4172d1e8d136e42866c087f2a8635d1b1935683f61ad2e4e33589cadf88ba","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-23T15:09:02Z","title_canon_sha256":"ca3eea2d68a664027f2a1d013666209450e83750adfc1b8ff955b65b850fb529"},"schema_version":"1.0","source":{"id":"2508.17056","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.17056","created_at":"2026-07-05T11:58:08Z"},{"alias_kind":"arxiv_version","alias_value":"2508.17056v1","created_at":"2026-07-05T11:58:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.17056","created_at":"2026-07-05T11:58:08Z"},{"alias_kind":"pith_short_12","alias_value":"R3BZTE2M7LNB","created_at":"2026-07-05T11:58:08Z"},{"alias_kind":"pith_short_16","alias_value":"R3BZTE2M7LNBSKJT","created_at":"2026-07-05T11:58:08Z"},{"alias_kind":"pith_short_8","alias_value":"R3BZTE2M","created_at":"2026-07-05T11:58:08Z"}],"graph_snapshots":[{"event_id":"sha256:087da333803feb6131294764d88ef52f7671294f30362af9a0e56b77b1a13af0","target":"graph","created_at":"2026-07-05T11:58:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2508.17056/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Tabular regression is a well-studied problem with numerous industrial applications, yet most existing approaches focus on point estimation, often leading to overconfident predictions. This issue is particularly critical in industrial automation, where trustworthy decision-making is essential. Probabilistic regression models address this challenge by modeling prediction uncertainty. However, many conventional methods assume a fixed-shape distribution (typically Gaussian), and resort to estimating distribution parameters. This assumption is often restrictive, as real-world target distributions c","authors_text":"Jonas Sonntag, Kiran Madhusudhanan, Lars Schmidt-Thieme, Maximilian Stubbemann, Vijaya Krishna Yalavarthi","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-23T15:09:02Z","title":"TabResFlow: A Normalizing Spline Flow Model for Probabilistic Univariate Tabular Regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.17056","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:7494329d01479ddc620c1e6b917a46f1ca6df4bc505534b30ff3114cbee2c790","target":"record","created_at":"2026-07-05T11:58:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f2f4172d1e8d136e42866c087f2a8635d1b1935683f61ad2e4e33589cadf88ba","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-23T15:09:02Z","title_canon_sha256":"ca3eea2d68a664027f2a1d013666209450e83750adfc1b8ff955b65b850fb529"},"schema_version":"1.0","source":{"id":"2508.17056","kind":"arxiv","version":1}},"canonical_sha256":"8ec399934cfada192933d464247205bc3ac01a29c145ee67fcab3dc90b2cb63d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8ec399934cfada192933d464247205bc3ac01a29c145ee67fcab3dc90b2cb63d","first_computed_at":"2026-07-05T11:58:08.812028Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:58:08.812028Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MagE5K0I+bBNFhdtXsWQaEZz93wg8/7/eC+HQUbaofkZ6h3866ctRG2tqKNPf9L2RRIeLZVWMBQTc4014dtBCg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:58:08.812428Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.17056","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7494329d01479ddc620c1e6b917a46f1ca6df4bc505534b30ff3114cbee2c790","sha256:087da333803feb6131294764d88ef52f7671294f30362af9a0e56b77b1a13af0"],"state_sha256":"32d6025cf5386241f7ca2ec7370af74887f0bd61421f88671b0018eab2986f83"}